The 3-Month Experiment: A Pragmatic Approach to Non-Scalable Solutions

In the realm of startup development, the notion of “doing things that don’t scale,” famously suggested by Paul Graham, is often echoed but seldom understood in practical terms—especially within the context of coding. After dedicating eight months to developing my AI podcast platform, I’ve crafted a straightforward approach to implementing this philosophy: every unscalable workaround I devise is given a lifespan of three months. At the end of this period, I either solidify its value by properly implementing it or discontinue it altogether.

Why the Traditional Approach Falls Short

Conventionally, as developers, we’re conditioned to prioritize scalable solutions from the outset. We immerse ourselves in grand architectures—design patterns, microservices, distributed systems—that are designed to support millions of users. While this mindset is valuable for larger enterprises, it can be counterproductive for a startup. In many cases, building scalable code early on can lead to unnecessary delays and resource expenditure, often preparing for an audience that doesn’t yet exist. Adopting my three-month principle has taught me the importance of writing straightforward, sometimes “subpar,” code that allows me to deliver quickly and discover what my users genuinely need.

Current Infrastructure Hacks: A Closer Look

1. Consolidated Operations on a Single VM

I’m operating my entire stack—a database, web server, background jobs, and Redis—on one $40/month virtual machine. This setup lacks redundancy and relies on manual backups to my local storage.

While this may seem reckless, it has provided invaluable insights about my resource requirements. Within two months, I learned that my AI-centric platform only surges to 4GB RAM usage, negating the complex Kubernetes architecture I nearly implemented. Each crash (there have been two) offered concrete data about failure points, and, unsurprisingly, they weren’t what I anticipated.

2. Hardcoded Configuration Parameters

Configuration can often become a labyrinth of files, but I have opted for simplicity: fixed constants throughout my codebase.

python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

By foregoing configuration files and environment variables, I can swiftly grep my code for any parameter. Since I’ve only changed these values three times in three months, the time saved in deployment—15 minutes versus the